Deep learning-based photodamage reduction on harmonic generation microscope at low-level optical power.

Journal: Journal of biophotonics
Published Date: Oct 2, 2023

Abstract

The trade-off between high-quality images and cellular health in optical bioimaging is a crucial problem. We demonstrated a deep-learning-based power-enhancement (PE) model in a harmonic generation microscope (HGM), including second harmonic generation (SHG) and third harmonic generation (THG). Our model can predict high-power HGM images from low-power images, greatly reducing the risk of phototoxicity and photodamage. Furthermore, the PE model trained only on normal skin data can also be used to predict abnormal skin data, enabling the dermatopathologist to successfully identify and label cancer cells. The PE model shows potential for in-vivo and ex-vivo HGM imaging.

Authors

  • Yi-Jiun Shen
    International Intercollegiate Ph.D. Program, National Tsing Hua University, Hsinchu, Taiwan.
  • En-Yu Liao
    Department of Electrical Engineering and Graduate Institute of Photonics and Optoelectronics, National Taiwan University, Taipei, Taiwan.
  • Tsung-Ming Tai
    NVIDIA AI Technology Center, NVIDIA, Taipei, Taiwan.
  • Yi-Hua Liao
    Department of Dermatology, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan.
  • Chi-Kuang Sun
    Department of Electrical Engineering and Graduate Institute of Photonics and Optoelectronics, National Taiwan University, Taipei, Taiwan.
  • Cheng-Kuang Lee
    NVIDIA AI Technology Center, NVIDIA, Taipei, Taiwan.
  • Simon See
    NVIDIA AI Technology Center, NVIDIA Corporation, Santa Clara, CA, United States.
  • Hung-Wen Chen
    Department of Psychology, National Taiwan University, Taipei, Taiwan.

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